Multi-Class Classification of Genetic Mutation Using Machine Learning Models
The challenge of distinguishing genetic mutations that contribute to tumor growth is crucial in cancer treatment. Cancer is responsible for millions of deaths annually, hence the need for early detection of tumors to improve treatment efficacy and survival rates. However, manual classification is prone to errors and inefficiencies due to human limitations and the complexity […]
A New Mixture of Two Components of Exponentiated Family with Applications to Real Life Data Sets
In this paper, the mixture of two components of exponentiated family is introduced as a new family of continuous distributions. Some general properties of the proposed family are discussed such as the quantile function, moments, moment generating function and order statistics. The maximum likelihood estimation method is used to derive the estimators for the unknown […]
Predicting Sleep Disorders: Leveraging Sleep Health and Lifestyle Data with Dipper Throated Optimization Algorithm for Feature Selection and Logistic Regression for Classification
This paper is a thorough examination of the modeling of sleep disorders based on machine learning that is applied to the sleep-health-and-lifestyle data. The use of the Dipper Throated Optimization Algorithm for feature selection and Logistic Regression for classification is the basis of the study that explores the effectiveness of predictive models in identifying sleep […]
A New Extention of the Odd Inverse Weibull-G Family of Distributions: Bayesian and Non-Bayesian Estimation with Engineering Applications
In this work, we propose a novel generator called the “extended odd inverse Weibull-generator” to obtain better distribution flexibility. This generator is considered as a generalization of the three well-known families. In comparison to the baseline model, the newly formed family may offer more efficient continuous symmetric and asymmetric models. The statistical features of the […]
On Fitting Renewable Energy Sources Data: Using a New Trigonometric Statistical Model
The goal of this work is to create an innovative heavy-tailed distribution known as the arctan-Kumaraswamy exponential (ATKE) distribution. The Kumaraswamy exponential distribution and the arctan-X family of distributions were combined to create the ATKE distribution. The ATKE distribution is adaptable and capable of modeling a range of hazard rate shapes when compared to the […]
A Proposed Distribution-Free Test For Symmetry of Grouped Data
This paper introduces a new, straightforward test for analyzing the symmetry of data represented in frequency distributions. Unlike other methods, this test doesn’t require any assumptions about the underlying statistical distribution of the data. The only prerequisite is that the data has equal-sized categories (bins) and none of these categories have a frequency of zero. […]
A Skew Product Distribution with Applications
The Rayleigh and error function distributions arise in many problems of applied and physical sciences. In this paper, we derive a skew type product Rayleigh-Error Function distribution by taking the product of the probability density function of Rayleigh distribution and the cumulative distribution function of the error function distribution. Several characteristics of the new distribution […]
A Novel Version of Geometric Distribution: Method and Application
This paper introduces a new family of discrete distributions, and investigates some of their statistical properties. The geometric distribution is utilized as a baseline for this new family, resulting in the derivation of a new discrete distribution, termed the generalized geometric distribution. This new distribution exhibits a wider range of shapes in its probability mass […]
An Improved Ant Colony Optimization to Uncover Customer Characteristics for Churn Prediction
Customer churn prediction is a critical task in the telecommunication (telecom) industry, where accurate identification of customers at risk of churning plays a vital role in reducing customer attrition. Feature selection (FS) is an integral part in Machine Learning (ML) models which aims to improve performance and reduce computational time (CT). This work optimizes Ant […]
A New Shifted Lomax-X Family of Distributions : Properties and Applications to Actuarial and Financial Data
The Lomax distribution, which is often used to describe severe losses and financial risks because of its heavy tail features, is the basis distribution of the shifted Lomax (SHL-X) family of distributions that we propose in this study. The main objective is to increase the adaptability and accuracy of the traditional Lomax model in the […]